Federated learning (FL) is increasingly recognised for addressing security and privacy concerns in traditional cloud-centric machine learning (ML), particularly within personalised health monitoring such as wearable devices. By enabling global model training through localised policies, FL allows resource-constrained wearables to operate independently. However, conventional first-order FL approaches face several challenges in personalised model training due to the heterogeneous non-independent and identically distributed (non-iid) data by each individual's unique physiology and usage patterns. Recently, second-order FL approaches maintain the stability and consistency of non-iid datasets while improving personalised model training. This study proposes and develops a verifiable and auditable optimised second-order FL framework BFEL (blockchain enhanced federated edge learning) based on optimised FedCurv for personalised healthcare systems. FedCurv incorporates information about the importance of each parameter to each client's task (through fisher information matrix) which helps to preserve client-specific knowledge and reduce model drift during aggregation. Moreover, it minimizes communication rounds required to achieve a target precision convergence for each client device while effectively managing personalised training on non-iid and heterogeneous data. The incorporation of ethereum-based model aggregation ensures trust, verifiability, and auditability while public key encryption enhances privacy and security. Experimental results of federated CNNs and MLPs utilizing mnist, cifar-10, and PathMnist demonstrate framework's high efficiency, scalability, suitability for edge deployment on wearables, and significant reduction in communication cost.

翻译：联邦学习（FL）因其能够解决传统以云为中心的机器学习（ML）中的安全与隐私问题而日益受到重视，尤其在可穿戴设备等个性化健康监测领域。通过本地化策略实现全局模型训练，FL使资源受限的可穿戴设备能够独立运行。然而，由于个体独特的生理特征和使用模式导致数据具有异构的非独立同分布（non-iid）特性，传统的一阶FL方法在个性化模型训练中面临诸多挑战。近期，二阶FL方法在提升个性化模型训练的同时，保持了non-iid数据集的稳定性和一致性。本研究提出并开发了一种基于优化FedCurv的可验证、可审计优化二阶FL框架BFEL（区块链增强的联邦边缘学习），用于个性化医疗系统。FedCurv通过费舍尔信息矩阵整合了每个参数对客户端任务的重要性信息，有助于保留客户端特定知识并减少聚合过程中的模型漂移。此外，它在有效管理non-iid异构数据个性化训练的同时，最小化了每个客户端设备达到目标精度收敛所需的通信轮次。基于以太坊的模型聚合机制确保了信任、可验证性与可审计性，而公钥加密则增强了隐私和安全性。利用MNIST、CIFAR-10和PathMNIST数据集对联邦CNN和MLP进行的实验结果表明，该框架具有高效性、可扩展性、适用于可穿戴设备边缘部署的特点，并能显著降低通信成本。